12/22/22 Healthcare Innovation – Year-End Review-AI: Artificial Intelligence Initiatives Accelerate in Healthcare
Amid the challenges of calendar year 2022, one of the bright spots was the acceleration in artificial intelligence-related activity—both clinical and non-clinical—in healthcare.
“Careful examination of the sepsis tool implementations have found that, when Suchi Saria’s team at Bayesian Health looked closely at the success levels of sepsis-alert algorithms, they found that the actual rates of improvement in intervention turned out to be far more modest than they appeared at first glance.
In fact, she said, ‘I’ve seen incorrect evaluation. People measured sepsis for mortality, then deployed the tool, then used billing code data, and evaluated. But it looks as though you’ve improved mortality, but there’s a dilution effect.’” In other words, it’s turning out that clinician and clinical informatics leaders must necessarily test out and recalibrate any algorithms developed elsewhere, in their own organizations, since, as Patterson told me, clinicians document inside their own organizations’ electronic health records in individual ways.
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12/20/22 Modern Healthcare – Navigating the ‘Wild West’ of AI adoption in healthcare
Right now, clinical AI adoption in healthcare can feel like the ‘wild west’ due to the lag in the regulator’s ability to keep pace with the dynamics within the marketplace. Consequently, health systems are taking matters into their own hands, forming internal guardrails to measure performance and substantiate AI investments across their clinical ecosystems. Ultimately, it comes down to innovation, risk-appetite and, most importantly, trust.
Our founder and CEO, Dr. Suchi Saria is quoted – “You can have the best technology in the world, but if [care teams] don’t trust it, they won’t use it, and you can’t see any benefit”.
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11/29/22 Forbes – Does Ethical AI Development Rely On The “Algorithmically” Underserved? CHAI’s Mission
CHAI co-founders Dr. Halamka and Dr. Anderson and CHAI member Suchi Saria of Bayesian Health discuss the importance and timeliness of CHAI’s mission, and share how the organization plans to prioritize patient safety, reliability, equity, transparency, and trust in the healthcare AI development process.
“AI as a field is evolving very rapidly. As a result, there is variable expertise amongst groups in how to go about implementing it correctly and evaluating whether what they’ve implemented is working. There is significant opportunity to accelerate AI adoption by sharing best practices and developing guardrails that the broader community (government, payor and provider groups) can benefit from.” Dr. Suchi Saria
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11/20/22 Healthcare In Europe – Early detection of sepsis with the help of AI
Suchi Saria, PhD, director of the Machine Learning and Healthcare Lab at Johns Hopkins, who led this work, explains that TREWS automatically and continuously monitors disparate clinical data, including vital signs, laboratory data, medication history, procedure and clinical history, and physician notes. It generates a continuous real-time “sepsis score” that can trigger an alert to healthcare staff. Clinical caregivers can analyse why the TREWS alert was generated, accept or dismiss it, and initiate timely treatment on patients confirmed to be septic.
‘Our results showing high physician adoption and associated mortality and morbidity reductions are a milestone for the field of AI,’ comments Saria. ‘They are the culmination of nearly a decade of significant technological investment, deep collaboration, the development of novel techniques, and rigorous evaluation. Further, what’s most exciting here is that this approach is applicable not just to sepsis but to many other critical complications.’
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10/16/22 The Atlantic – Doctors Still Struggle to Diagnose a Condition That Kills More Americans Than Stroke
In July, Johns Hopkins researchers published a trio of studies in Nature Medicine and npj Digital Medicine showcasing an early-warning system that uses artificial intelligence. The system caught 82 percent of sepsis cases and significantly reduced mortality. While AI—in this case, machine learning—has long promised to improve health care, most studies demonstrating its benefits have been conducted using historical data sets. Sources told me that, to the best of their knowledge, when used on patients in real time, no AI algorithm has shown success at scale.
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Learn more about Bayesian’s peer-reviewed research on sepsis here
10/12/22 Smithsonian Magazine – A New, Transparent AI Tool May Help Detect Blood Poisoning
The most impressive aspect of TREWS, according to Zachary Lipton, an assistant professor of machine learning and operations research at Carnegie Mellon University, was not the model’s novelty, but the effort it must have taken to deploy it across five hospitals and 2,000 providers over a two-year period. “In this area, there is a tremendous amount of offline research,” Lipton said, but relatively few studies “actually make it to the level of being deployed widely in a major health system.” It’s so difficult to perform “in the wild” research like this, he added, because it requires collaborations across various disciplines, from product designers to systems engineers to administrators
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Read the Smithsonian Magazine article
Learn more about Bayesian’s peer-reviewed research on sepsis here
09/26/22 AMA – What it takes for doctors to trust AI-triggered sepsis alerts
Increasing adoption in sepsis AI with Bayesian. “This is a breakthrough in many ways,” said study co-author Albert W. Wu, MD, director of the Center for Health Services and Outcomes Research at Johns Hopkins Bloomberg School of Public Health. “Up to this point, most of these types of systems have guessed wrong much more often than they get it right. Those false alarms undermine confidence.”
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Learn more about Bayesian’s peer-reviewed research on sepsis here
09/08/22 Signify Research – Early Warning for Sepsis from Bayesian Health – Advances of AI in Clinical Care Continue
Rigorous evaluation of AI reducing sepsis mortality. Bayesian Health’s solution has demonstrated the positive outcomes that AI-based applications can have on detecting sepsis and reducing risk of mortality. The clinical evidence to support the positive outcomes from utilizing the platform is expected to create momentum within the industry, with other vendors striving to support their own solutions with proven clinical outcomes.
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Read the Signify Research article
Learn more about Bayesian’s peer-reviewed research on sepsis here
08/11/22 Becker’s Hospital Review – Early warnings, few false alerts: What physicians want out of AI sepsis detection tools
Mitigating sepsis risk with AI machine learning. “This tool makes every clinician more efficient and can let you manage more patients because there’s less work to do,” said Lee Sacks, MD, founding CEO of Advocate Physician Partners and former system chief medical officer of Downers Grove, Ill., and Milwaukee-based Advocate Aurora Health. “So I think, going forward … [staff] are going to choose to work in organizations that have a tool like this because it makes them more effective.”
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Learn more about Bayesian’s peer-reviewed research on sepsis here
